Lab Sessions Variance Mean the average score add all the scores together and divide by of scores Median the score in the middle Mode the most frequent score Standard deviation the square root of the variance deviation from the mean Variance s 2 the sum of squares divided by sample size the spread of the numbers The components of variance in your data Total variance systematic variance error variance Total variance observed differences Systematic variance effect of IV Error variance variability in scores caused by extraneous variables or participant variability effect of all other things Why is error variance a problem Could show effect of IV when in fact there wasn t one or could show a greater effect than there actually is Where does error variance come from Extraneous scores or participant variability Regularize experiment to remove error variance Reduce sources of error variance Four ways to deal with error variance Control it Treat all participants the same Match participants on variables that have an effect on DV Randomize it across conditions Make it so participants have an equal chance of being in any group Increase effectiveness of your IV Use levels of IV that are very different Account for it with statistics Estimate the probability that error variance caused the observed effect Designs Randomized 2 Group Designs Advantages Simple few participants required easy to interpret no pre test required to ensure equality of groups Disadvantages doesn t yield a large amount of info insensitive to effects when participants differ greatly in performance Randomized Multi Group Designs Properties comparing two or more treatments to one or more control groups Advantages permit comparing two or more treatments to one or more control groups Disadvantages multiple control groups necessary to rule out alternative explanations Matched Pair Properties Measure sample and find pairs of people who match on characteristics that might influence performance for each pair randomly assign which person goes into which group Matched Multi Group Designs Properties 3 or more groups find similar participants than randomly assign these participants to groups Advantages help to control for error variance by matching groups on characteristics that influence performance Disadvantages difficult to implement require use of different statistics Within Participant Designs Properties same subjects used in all conditions Advantages reduces error variance due to individual differences among subjects across treatment groups requires fewer participants Disadvantages more demand put on participants carry over effects Factorial Designs Properties more than one IV use either between or within participant IVs Advantages more efficient greater external validity can test main effects and interactions Disadvantages more complex more participants difficult to interpret Repeated measures is another way to say within subjects Matched Design Advantages Carryover Effects Controls participant related variability Matches groups on characteristics that influence performance Systematic variance is easier to observe in this design Hurts ability to detect an effect Need to measure all participants before study Many groups difficult to find people who match Exposure to a previous treatment affects performance in a subsequent treatment Ways to deal with them Minimize their effect introduce breaks into the study to allow effect of previous treatment to wear off practice trials Counterbalance varying the order of treatments Advantage all possible combinations of treatments are represented Disadvantage minimum number of subjects can get large fast Include treatment order as an IV will let you know if significant carryover effects are present in your study Carryover effects can sometimes not be dealt with When treatments have irreversible effects Different carryover effects Example Treatment A has a different effect on B than B does on A Methods of Counterbalancing Partial Counterbalancing includes some of the possible treatment orders but not all Reverse counterbalancing use with small number of conditions Random Order counterbalancing assign random order of treatment to each participant Latin square design Controls for ordinal position of each treatment Each condition follows each other condition once Factorial Design Properties Has more than 1 IV Can use within or between participant IVs Main effect the separate effect of each independent variable Main effect the number of factors Interaction the effect of one IV changes based on the level of another IV Signature of an interaction graphically Interaction if the two lines are parallel or perpendicular cross in the middle Nomenclature of Factorial Designs Factor each IV Levels each factor has a number of levels treatments Condition a specific combination of levels In a given design Number of potential main effects of IVs Number of interactions number of possible combinations of IVs Example 2 x 3 x 2 3 factors 7 levels 12 conditions 3 main effects 4 interactions Specific Factorial Design Participants receive all the treatments of one IV and only some of the other MIXED design Main limitation of a factorial design when the number of IVs is 3 or more difficult to interpret Covariate Combining correlation and experimental methods The idea of holding something constant to reduce error variance Measuring something to get rid of it Example If there is a systematic relationship between IQ and our measure we should be able to predict what everyone s score would have been if they were all the same IQ Quasi independent Variable Including measured or subject variables in your experiment IV is not manipulated Advantages of including one in your study We can look at the generalizability of a result Like a covariate it can suck up some of the variance in the data and allow us to see the effect of our IV by grouping together participants who behaved similarly Biggest problem associated with a quasi IV Causation cannot be inferred whenever a subject variable is in your model you cannot infer causation Threat to internal validity Book Objectives Chapter 8 Inferential Statistics Tell us if the difference between groups is larger than expected due to chance or error variance Used to test a research hypothesis because they estimate the probability that the results were due to chance The null hypothesis H0 states that there is no difference between groups which means that the independent variable had no